Traditional survival analysis methods include the Cox Proportional Hazards model (CPH) [1]}. CPH is basically a linear regression model that predicts simultaneously the effect of several risk factors on survival time. However, these standard survival models encounter some challenges when it comes to real-world datasets.
For instance, they cannot easily capture nonlinear relationships between the covariates. In addition, in many applications, the presence of high-dimensional data is quite common, e.g., gene expression data; however, these traditional methods are not able to efficiently deal with such high-dimensional data. As a result, machine learning-based techniques have become increasingly popular in the survival analysis context over recent years [2]}. Applying machine learning methods directly to censored data is challenging since the value of a measurement or observation is only partially known. Several studies have successfully modified machine learning algorithms to make use of censored information in survival analysis, e.g., decision trees [3]}, artificial neural networks (ANN) [4]}, and support vector machines (SVM) [5]} to name a few. Popular ensemble-based frameworks include bagging survival trees [6]} and random survival forests [7]}. Also, more advanced learning tasks such as active learning [8]} and transfer learning [9]} have been extended toward survival analysis.
